R/Placebo-combined.R
In nicksun1/Biomarker-Analysis: MMRM Analysis of New Biomarker
# Biomarker Regression Analysis
#
# Log transformed analysis of combined biomarker data from studies A, CF, I, G
# Outputs csv file including Treatment, Number of Patients, Mean of log(biomarker), SE of log(biomarker), and 95% confidence intervals
# Results of interest include %Change vs Placebo and Change in log(biomarker) vs Placebo for separate doses of study drug as well as competitor drugs.
#
# Fit to model Change in log(biomarker) = Treatment
##
library(dplyr)
library(zoo)
library(EnvStats)
library(reshape)
suppressMessages(library(coastr))
## studycf
lab <- read.csv("labscf.csv")
studycf <- lab %>% select(SUBJID, VISID, TRT, TRTSORT, LBTESTABR, LBTEST, LBRN, LBBLVALTR,LBRUCD)
studycf <- studycf %>% filter(LBTESTABR =="MAL/CR")
trt_merge <- studycf %>% filter (LBRUCD=="95")
trt_merge <- trt_merge %>% filter( VISID =="9") ###################
trt_merge <- trt_merge[complete.cases(trt_merge[,4]),]
trt_merge$aval_unchanged <- trt_merge$LBRN
trt_merge$base_unchanged <- trt_merge$LBBLVALTR
trt_merge$AVAL <- log(trt_merge$LBRN)
trt_merge$BASE <- log(trt_merge$LBBLVALTR)
trt_merge$Change <- trt_merge$AVAL - trt_merge$BASE
trt_merge$Change_oriscale <- trt_merge$aval_unchanged-trt_merge$base_unchanged
trt_merge$Percent_change3 <- log(trt_merge$aval_unchanged/trt_merge$base_unchanged)
trt_merge <- trt_merge[complete.cases(trt_merge[,14]),]
trt_merge <- trt_merge[,c(-4,-5,-7,-8,-9)]
studycf_comb <- trt_merge
studycf_comb$SUBJID <- paste("studycf",studycf_comb$SUBJID, sep="_")
## studya
lab <- read.csv("labsa.csv")
studya <- lab %>% select(SUBJID, VISID, TRT, TRTSORT, LBTESTABR, LBTEST, LBRN, LBBLVALTR,LBRUCD)
studya <- studya %>% filter(LBTESTABR =="MAL/CR")
trt_merge <- studya %>% filter (LBRUCD=="95")
trt_merge <- trt_merge %>% filter(VISID =="1" |VISID =="10")
#trt_merge <- merge(studya, adsl_trt,all=TRUE)
trt_merge <- trt_merge[complete.cases(trt_merge[,4]),]
trt_merge$aval_unchanged <- trt_merge$LBRN
trt_merge$base_unchanged <- trt_merge$LBBLVALTR
trt_merge$AVAL <- log(trt_merge$LBRN)
trt_merge$BASE <- log(trt_merge$LBBLVALTR)
trt_merge$Change <- trt_merge$AVAL - trt_merge$BASE
trt_merge$Change_oriscale <- trt_merge$aval_unchanged-trt_merge$base_unchanged
trt_merge$Percent_change3 <- log(trt_merge$aval_unchanged/trt_merge$base_unchanged)
trt_merge <- trt_merge[,c(-4,-5,-7,-8,-9)]
studya_comb <- trt_merge
studya_comb$SUBJID <- paste("studya",studya_comb$SUBJID, sep="_")
## studyg
lab <- read.csv("labsg.csv")
adsl <- read.csv("adslg.csv")
studyg <- lab %>% select(USUBJID, AVISIT, AVISITN, PARAM, PARAMCD, AVAL, BASE, SAFFL)
studyg <- studyg %>% filter(PARAMCD =="ALBCS49C")
studyg <- studyg %>% filter(SAFFL=="Y")
studyg <- studyg %>% filter(AVISITN =="9" |AVISITN =="100")
adsl_trt <- adsl %>% select(USUBJID ,TRT01A, TRT01AN)
trt_merge <- merge(studyg, adsl_trt,all=TRUE)
trt_merge <- trt_merge[complete.cases(trt_merge),]
trt_merge <- trt_merge %>% filter(SAFFL=="Y")
trt_merge$aval_unchanged <- trt_merge$AVAL
trt_merge$base_unchanged <- trt_merge$BASE
trt_merge$AVAL <- log(trt_merge$AVAL)
trt_merge$BASE <- log(trt_merge$BASE)
trt_merge$Change <- trt_merge$AVAL - trt_merge$BASE
trt_merge$Change_oriscale <- trt_merge$aval_unchanged-trt_merge$base_unchanged
trt_merge$Percent_change3 <- log(trt_merge$aval_unchanged/trt_merge$base_unchanged)
trt_merge <- trt_merge[,c(-2,-5,-10,-8)]
trt_merge <- trt_merge[,c(1,2,6,3,7,8,4,5,9,10,11)]
names(trt_merge) <- c(colnames(studycf_comb))
studyg_comb <- trt_merge
## studyi
lab <- read.csv("labsi.csv")
adsl <- read.csv("adsli.csv")
studyi <- lab %>% select(USUBJID, AVISIT, AVISITN, PARAM, PARAMCD, AVAL, BASE, SAFFL)
studyi <- studyi %>% filter(PARAMCD =="ALBCS49C")
studyi <- studyi %>% filter(SAFFL=="Y")
studyi <- studyi %>% filter(AVISITN =="100" |AVISITN =="204")
adsl_trt <- adsl %>% select(USUBJID ,TRT01A, TRT01AN)
trt_merge <- merge(studyi, adsl_trt,all=TRUE)
trt_merge <- trt_merge[complete.cases(trt_merge),]
trt_merge <- trt_merge %>% filter(SAFFL=="Y")
trt_merge$aval_unchanged <- trt_merge$AVAL
trt_merge$base_unchanged <- trt_merge$BASE
trt_merge$AVAL <- log(trt_merge$AVAL)
trt_merge$BASE <- log(trt_merge$BASE)
trt_merge$Change <- trt_merge$AVAL - trt_merge$BASE
trt_merge$Change_oriscale <- trt_merge$aval_unchanged-trt_merge$base_unchanged
trt_merge$Percent_change3 <- log(trt_merge$aval_unchanged/trt_merge$base_unchanged)
trt_merge <- trt_merge[,c(-2,-5,-10,-8)]
trt_merge <- trt_merge[,c(1,2,6,3,7,8,4,5,9,10,11)]
names(trt_merge) <- c(colnames(studycf_comb))
studyi_comb <- trt_merge
combined <- merge(studycf_comb, studya_comb, all=TRUE)
combined <- merge(combined, studyi_comb, all=TRUE)
combined <- merge(combined, studyg_comb, all=TRUE)
combined$Threshold <- ifelse(combined$base_unchanged>30, "Above 30 Baseline", "Below 30 Baseline")
combined_above <- combined %>% filter (base_unchanged>30)
## Above 30
combined_above$TRT<-as.factor(combined_above$TRT)
combined_above$TRT = relevel(combined_above$TRT, ref="Placebo")
lm_data <- combined_above
lm_data$Change[lm_data$Change==0]<- NA
lm_data <- lm_data[complete.cases(lm_data[,9]),]
fit <- lm(Change~TRT, data=lm_data)
coefficients <-data.frame(summary(fit)$coefficients)
vsplacebo_glargine <- data.frame(coefficients[2:5,1:2])
names(vsplacebo_glargine) <-c("AVAL", "SE","TRT","VISID")
vsplacebo_glargine2 <- vsplacebo_glargine %>% group_by(TRT,VISID) %>% summarise( geomean =exp(AVAL)-1, geoSE= exp(AVAL)*SE)
ci<-data.frame(confint(fit,level=0.95))
ci <- data.frame(ci[2:5,1:2])
names(ci) <-c("Lower", "Upper","TRT","VISID")
ci_hold <- ci %>% group_by(TRT,VISID) %>% summarise( Lower =exp(Lower)-1, Upper=exp(Upper)-1)
coeff_change <-data.frame(summary(fit)$coefficients)
trt_diff <- data.frame(coeff_change[2:5,1:2])
names(trt_diff) <-c("geomean", "geoSE","TRT","VISID")
ci1<-data.frame(confint(fit,level=0.95))
ci1 <- data.frame(ci1[2:5,1:2])
names(ci1) <-c("Lower", "Upper","TRT","VISID")
ci_merge <- merge(ci_hold, ci1,all=TRUE)
trt_diff_merge <-merge(trt_diff,vsplacebo_glargine2,all=TRUE)
final_diff <- merge(ci_merge, trt_diff_merge,all=TRUE)
final_diff[c(1,3,5,7),3:6]<-final_diff[c(1,3,5,7),3:6]*100
final_diff$Threshold <- "Above 30 Baseline"
final_diff<- final_diff[,c(7,1,2,5,6,3,4)]
names(final_diff) <- c("Threshold","Treatment", "Comparison", "Mean", "SE","Lower","Upper")
## Below 30 Baseline
combined_below <- combined %>% filter (base_unchanged<30)
combined_below$TRT<-as.factor(combined_below$TRT)
combined_below$TRT = relevel(combined_below$TRT, ref="Placebo")
lm_data <- combined_below
lm_data$Change[lm_data$Change==0]<- NA
lm_data <- lm_data[complete.cases(lm_data[,9]),]
fit <- lm(Change~TRT, data=lm_data)
coefficients <-data.frame(summary(fit)$coefficients)
vsplacebo_glargine <- data.frame(coefficients[2:5,1:2])
names(vsplacebo_glargine) <-c("AVAL", "SE","TRT","VISID")
vsplacebo_glargine2 <- vsplacebo_glargine %>% group_by(TRT,VISID) %>% summarise( geomean =exp(AVAL)-1, geoSE= exp(AVAL)*SE)
ci<-data.frame(confint(fit,level=0.95))
ci <- data.frame(ci[2:5,1:2])
names(ci) <-c("Lower", "Upper","TRT","VISID")
ci_hold <- ci %>% group_by(TRT,VISID) %>% summarise( Lower =exp(Lower)-1, Upper=exp(Upper)-1)
coeff_change <-data.frame(summary(fit)$coefficients)
trt_diff <- data.frame(coeff_change[2:5,1:2])
names(trt_diff) <-c("geomean", "geoSE","TRT","VISID")
ci1<-data.frame(confint(fit,level=0.95))
ci1 <- data.frame(ci1[2:5,1:2])
names(ci1) <-c("Lower", "Upper","TRT","VISID")
ci_merge <- merge(ci_hold, ci1,all=TRUE)
trt_diff_merge <-merge(trt_diff,vsplacebo_glargine2,all=TRUE)
final_diff_below <- merge(ci_merge, trt_diff_merge,all=TRUE)
final_diff_below[c(1,3,5,7),3:6]<-final_diff_below[c(1,3,5,7),3:6]*100
final_diff_below$Threshold <- "Below 30 Baseline"
final_diff_below<- final_diff_below[,c(7,1,2,5,6,3,4)]
names(final_diff_below) <- c("Threshold","Treatment", "Comparison", "Mean", "SE","Lower","Upper")
## Combine
output_placebo <- merge(final_diff,final_diff_below,all=TRUE)
write.csv(output_placebo, "Placebo_compare_threshold30.csv")
nicksun1/Biomarker-Analysis documentation built on Dec. 22, 2021, 2:14 a.m.
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# Biomarker Regression Analysis
#
# Log transformed analysis of combined biomarker data from studies A, CF, I, G
# Outputs csv file including Treatment, Number of Patients, Mean of log(biomarker), SE of log(biomarker), and 95% confidence intervals
# Results of interest include %Change vs Placebo and Change in log(biomarker) vs Placebo for separate doses of study drug as well as competitor drugs.
#
# Fit to model Change in log(biomarker) = Treatment
##
library(dplyr)
library(zoo)
library(EnvStats)
library(reshape)
suppressMessages(library(coastr))
## studycf
lab <- read.csv("labscf.csv")
studycf <- lab %>% select(SUBJID, VISID, TRT, TRTSORT, LBTESTABR, LBTEST, LBRN, LBBLVALTR,LBRUCD)
studycf <- studycf %>% filter(LBTESTABR =="MAL/CR")
trt_merge <- studycf %>% filter (LBRUCD=="95")
trt_merge <- trt_merge %>% filter( VISID =="9") ###################
trt_merge <- trt_merge[complete.cases(trt_merge[,4]),]
trt_merge$aval_unchanged <- trt_merge$LBRN
trt_merge$base_unchanged <- trt_merge$LBBLVALTR
trt_merge$AVAL <- log(trt_merge$LBRN)
trt_merge$BASE <- log(trt_merge$LBBLVALTR)
trt_merge$Change <- trt_merge$AVAL - trt_merge$BASE
trt_merge$Change_oriscale <- trt_merge$aval_unchanged-trt_merge$base_unchanged
trt_merge$Percent_change3 <- log(trt_merge$aval_unchanged/trt_merge$base_unchanged)
trt_merge <- trt_merge[complete.cases(trt_merge[,14]),]
trt_merge <- trt_merge[,c(-4,-5,-7,-8,-9)]
studycf_comb <- trt_merge
studycf_comb$SUBJID <- paste("studycf",studycf_comb$SUBJID, sep="_")
## studya
lab <- read.csv("labsa.csv")
studya <- lab %>% select(SUBJID, VISID, TRT, TRTSORT, LBTESTABR, LBTEST, LBRN, LBBLVALTR,LBRUCD)
studya <- studya %>% filter(LBTESTABR =="MAL/CR")
trt_merge <- studya %>% filter (LBRUCD=="95")
trt_merge <- trt_merge %>% filter(VISID =="1" |VISID =="10")
#trt_merge <- merge(studya, adsl_trt,all=TRUE)
trt_merge <- trt_merge[complete.cases(trt_merge[,4]),]
trt_merge$aval_unchanged <- trt_merge$LBRN
trt_merge$base_unchanged <- trt_merge$LBBLVALTR
trt_merge$AVAL <- log(trt_merge$LBRN)
trt_merge$BASE <- log(trt_merge$LBBLVALTR)
trt_merge$Change <- trt_merge$AVAL - trt_merge$BASE
trt_merge$Change_oriscale <- trt_merge$aval_unchanged-trt_merge$base_unchanged
trt_merge$Percent_change3 <- log(trt_merge$aval_unchanged/trt_merge$base_unchanged)
trt_merge <- trt_merge[,c(-4,-5,-7,-8,-9)]
studya_comb <- trt_merge
studya_comb$SUBJID <- paste("studya",studya_comb$SUBJID, sep="_")
## studyg
lab <- read.csv("labsg.csv")
adsl <- read.csv("adslg.csv")
studyg <- lab %>% select(USUBJID, AVISIT, AVISITN, PARAM, PARAMCD, AVAL, BASE, SAFFL)
studyg <- studyg %>% filter(PARAMCD =="ALBCS49C")
studyg <- studyg %>% filter(SAFFL=="Y")
studyg <- studyg %>% filter(AVISITN =="9" |AVISITN =="100")
adsl_trt <- adsl %>% select(USUBJID ,TRT01A, TRT01AN)
trt_merge <- merge(studyg, adsl_trt,all=TRUE)
trt_merge <- trt_merge[complete.cases(trt_merge),]
trt_merge <- trt_merge %>% filter(SAFFL=="Y")
trt_merge$aval_unchanged <- trt_merge$AVAL
trt_merge$base_unchanged <- trt_merge$BASE
trt_merge$AVAL <- log(trt_merge$AVAL)
trt_merge$BASE <- log(trt_merge$BASE)
trt_merge$Change <- trt_merge$AVAL - trt_merge$BASE
trt_merge$Change_oriscale <- trt_merge$aval_unchanged-trt_merge$base_unchanged
trt_merge$Percent_change3 <- log(trt_merge$aval_unchanged/trt_merge$base_unchanged)
trt_merge <- trt_merge[,c(-2,-5,-10,-8)]
trt_merge <- trt_merge[,c(1,2,6,3,7,8,4,5,9,10,11)]
names(trt_merge) <- c(colnames(studycf_comb))
studyg_comb <- trt_merge
## studyi
lab <- read.csv("labsi.csv")
adsl <- read.csv("adsli.csv")
studyi <- lab %>% select(USUBJID, AVISIT, AVISITN, PARAM, PARAMCD, AVAL, BASE, SAFFL)
studyi <- studyi %>% filter(PARAMCD =="ALBCS49C")
studyi <- studyi %>% filter(SAFFL=="Y")
studyi <- studyi %>% filter(AVISITN =="100" |AVISITN =="204")
adsl_trt <- adsl %>% select(USUBJID ,TRT01A, TRT01AN)
trt_merge <- merge(studyi, adsl_trt,all=TRUE)
trt_merge <- trt_merge[complete.cases(trt_merge),]
trt_merge <- trt_merge %>% filter(SAFFL=="Y")
trt_merge$aval_unchanged <- trt_merge$AVAL
trt_merge$base_unchanged <- trt_merge$BASE
trt_merge$AVAL <- log(trt_merge$AVAL)
trt_merge$BASE <- log(trt_merge$BASE)
trt_merge$Change <- trt_merge$AVAL - trt_merge$BASE
trt_merge$Change_oriscale <- trt_merge$aval_unchanged-trt_merge$base_unchanged
trt_merge$Percent_change3 <- log(trt_merge$aval_unchanged/trt_merge$base_unchanged)
trt_merge <- trt_merge[,c(-2,-5,-10,-8)]
trt_merge <- trt_merge[,c(1,2,6,3,7,8,4,5,9,10,11)]
names(trt_merge) <- c(colnames(studycf_comb))
studyi_comb <- trt_merge
combined <- merge(studycf_comb, studya_comb, all=TRUE)
combined <- merge(combined, studyi_comb, all=TRUE)
combined <- merge(combined, studyg_comb, all=TRUE)
combined$Threshold <- ifelse(combined$base_unchanged>30, "Above 30 Baseline", "Below 30 Baseline")
combined_above <- combined %>% filter (base_unchanged>30)
## Above 30
combined_above$TRT<-as.factor(combined_above$TRT)
combined_above$TRT = relevel(combined_above$TRT, ref="Placebo")
lm_data <- combined_above
lm_data$Change[lm_data$Change==0]<- NA
lm_data <- lm_data[complete.cases(lm_data[,9]),]
fit <- lm(Change~TRT, data=lm_data)
coefficients <-data.frame(summary(fit)$coefficients)
vsplacebo_glargine <- data.frame(coefficients[2:5,1:2])
names(vsplacebo_glargine) <-c("AVAL", "SE","TRT","VISID")
vsplacebo_glargine2 <- vsplacebo_glargine %>% group_by(TRT,VISID) %>% summarise( geomean =exp(AVAL)-1, geoSE= exp(AVAL)*SE)
ci<-data.frame(confint(fit,level=0.95))
ci <- data.frame(ci[2:5,1:2])
names(ci) <-c("Lower", "Upper","TRT","VISID")
ci_hold <- ci %>% group_by(TRT,VISID) %>% summarise( Lower =exp(Lower)-1, Upper=exp(Upper)-1)
coeff_change <-data.frame(summary(fit)$coefficients)
trt_diff <- data.frame(coeff_change[2:5,1:2])
names(trt_diff) <-c("geomean", "geoSE","TRT","VISID")
ci1<-data.frame(confint(fit,level=0.95))
ci1 <- data.frame(ci1[2:5,1:2])
names(ci1) <-c("Lower", "Upper","TRT","VISID")
ci_merge <- merge(ci_hold, ci1,all=TRUE)
trt_diff_merge <-merge(trt_diff,vsplacebo_glargine2,all=TRUE)
final_diff <- merge(ci_merge, trt_diff_merge,all=TRUE)
final_diff[c(1,3,5,7),3:6]<-final_diff[c(1,3,5,7),3:6]*100
final_diff$Threshold <- "Above 30 Baseline"
final_diff<- final_diff[,c(7,1,2,5,6,3,4)]
names(final_diff) <- c("Threshold","Treatment", "Comparison", "Mean", "SE","Lower","Upper")
## Below 30 Baseline
combined_below <- combined %>% filter (base_unchanged<30)
combined_below$TRT<-as.factor(combined_below$TRT)
combined_below$TRT = relevel(combined_below$TRT, ref="Placebo")
lm_data <- combined_below
lm_data$Change[lm_data$Change==0]<- NA
lm_data <- lm_data[complete.cases(lm_data[,9]),]
fit <- lm(Change~TRT, data=lm_data)
coefficients <-data.frame(summary(fit)$coefficients)
vsplacebo_glargine <- data.frame(coefficients[2:5,1:2])
names(vsplacebo_glargine) <-c("AVAL", "SE","TRT","VISID")
vsplacebo_glargine2 <- vsplacebo_glargine %>% group_by(TRT,VISID) %>% summarise( geomean =exp(AVAL)-1, geoSE= exp(AVAL)*SE)
ci<-data.frame(confint(fit,level=0.95))
ci <- data.frame(ci[2:5,1:2])
names(ci) <-c("Lower", "Upper","TRT","VISID")
ci_hold <- ci %>% group_by(TRT,VISID) %>% summarise( Lower =exp(Lower)-1, Upper=exp(Upper)-1)
coeff_change <-data.frame(summary(fit)$coefficients)
trt_diff <- data.frame(coeff_change[2:5,1:2])
names(trt_diff) <-c("geomean", "geoSE","TRT","VISID")
ci1<-data.frame(confint(fit,level=0.95))
ci1 <- data.frame(ci1[2:5,1:2])
names(ci1) <-c("Lower", "Upper","TRT","VISID")
ci_merge <- merge(ci_hold, ci1,all=TRUE)
trt_diff_merge <-merge(trt_diff,vsplacebo_glargine2,all=TRUE)
final_diff_below <- merge(ci_merge, trt_diff_merge,all=TRUE)
final_diff_below[c(1,3,5,7),3:6]<-final_diff_below[c(1,3,5,7),3:6]*100
final_diff_below$Threshold <- "Below 30 Baseline"
final_diff_below<- final_diff_below[,c(7,1,2,5,6,3,4)]
names(final_diff_below) <- c("Threshold","Treatment", "Comparison", "Mean", "SE","Lower","Upper")
## Combine
output_placebo <- merge(final_diff,final_diff_below,all=TRUE)
write.csv(output_placebo, "Placebo_compare_threshold30.csv")
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